Ask your IoT data anything.
No dashboards to build. No queries to write. No data scientist required. PixelAssistant turns your live and historical IoT data into a simple conversation — ask in plain language and get the answer — chart, cause, and next step.
One assistant, every team
Ask in plain language
Type a question the way you'd ask a colleague — no query language, no dashboard to build first. Every team can self-serve their own data.
Cause, not just a number
Each answer arrives with the complete picture — affected zones, recommended action — plus one-tap follow-ups and comparisons.
It watches while you don't
Anomaly detectors and scheduled workflows run in the background, delivering insights automatically — no need to ask.
Just ask
From a question to a decision, in one thread
The same assistant handles the quick lookup, the proactive heads-up and the deep dive — here it is, exactly as your team sees it.
Answers with cause and next step
Every reply comes with the chart, affected zones and a recommended action, plus quick buttons to drill down, notify a team or save a report.
- Cause analysis, not just raw data
- One-click follow-ups and comparisons
- Export to PDF or save as a report
May is tracking under last month's pace overall, but two areas stand out. Building 2's conference rooms are running HVAC outside booked hours — that drove the May 14 all-buildings peak of 9.4 MWh. And water use at Greenhouse cluster B is up about 12% week-over-week.
See what matters before you search
Watchers and anomaly detectors run scheduled prompts through the model and present findings as cards. Pull any card straight into a conversation.
- Scheduled scenarios & watchers
- Severity-ranked, auto-resolving findings
- One tap to investigate in chat
Insights
6 active · 11 resolved todayLive findings from watchers, anomaly detectors and scheduled scenarios. Click any card to pull it into a conversation.
Climate units in rooms 4-201 to 4-208 have been running since 06:00 — three hours before the first booked meeting. Estimated waste this morning: 42 kWh (~€8).
Pull into chatMean indoor temperature across 6 zones in the North wing is consistently above the comfort band. HVAC schedule unchanged — pattern suggests a stuck damper or rooftop air handler.
Pull into chatRoom is booked frequently but actual occupancy stays low — typical "ghost meeting" pattern. Consider shrinking capacity in the booking tool or repurposing as a quiet zone.
Pull into chatAir moisture is climbing despite stable outdoor weather. The current irrigation cycle could likely be shortened by 12–18 minutes without affecting target soil moisture.
Pull into chatLights stay on past 23:00 three nights this week with no occupancy detected. Suggest tightening the auto-off window or adding a motion override.
Pull into chatFlow was at 2.4× baseline for ~50 minutes. Coincides with the weekly deep-clean window — pattern matches the last three Mondays. No leak, no action needed.
Pull into chatSave your go-to scenarios and run them in one click
A workflow is a preset of prompts the assistant runs in sequence — save the questions you ask most often once, then rerun the whole flow whenever you need it.
- Multi-step prompt presets, run top to bottom
- Save your most-used scenarios once
- One click to rerun an entire sequence
Scheduled checks that watch the system for you
A watcher is a saved prompt sequence the assistant runs on a schedule — so you get a status report on time, or a check fires and notifies you the moment something drifts.
- Prompt sequences run by the LLM on a cron
- Scheduled status reports & health checks
- Notifies you only when something needs attention
LLM reasoning over a live GraphQL layer
PixelMonitor classifies and models the data upfront, so the assistant can query it safely and answer in real time, eliminating the need for a separate analytics pipeline.
Your question
Plain language, any team
Assistant Agent
LLM plans the query and structures the answer
GraphQL layer
Live objects, properties and history
PixelCore
PostgreSQL · TimescaleDB